Livelihoods and the Allocation of Emergency Assistance after the Haiti Earthquake
Bibliographic record
Abstract
In this paper, a unique post-earthquake survey designed to provide a rapid assessment of food insecurity in Haiti is used in order to see how adequately emergency assistance programs have been allocated. When modelling the impact of various covariates upon assistance allocation, the location of households emerges as the main criterion. This helps to explain why, five months after the quake, government and agencies still seemed unable to provide an efficient allocation of emergency assistance. What is more, those who benefited less from assistance appeared to be on the one hand families headed by women and on the other hand households with disabled members: this obviously runs counter to an "optimal" targeting that would make the most vulnerable ones eligible for assistance in priority. Furthermore, the fact that associations may favour assistance allocation is an interesting result that should be considered further. It is also found that asset losses had no significant impact on the food consumption score, whereas household pre-earthquake wealth did. This result demonstrates that the impact of the shock has been buffered when households had previously enforced coping strategies, regardless of the effects of emergency assistance programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".